Clinical Efficacy of Programmed Cell Death Ligand 1 Antibody in Treatment of Extranodal Natural Killer/T-Cell Lymphoma With Hemophagocytic Lymphohistiocytosis
Bibliographic record
Abstract
Extranodal natural killer/T-cell lymphoma-associated hemophagocytic lymphohistiocytosis (ENKTCL-LAHS) is a rare disease with poor prognosis. Currently, there are no well-established treatments for LAHS. Almost 50% of patients experience relapsed or refractory disease to anti-hemophagocytic lymphohistiocytosis (HLH) treatment, and the regimen for salvage therapy is limited. We report a case of ENKTCL-LAHS that was successfully treated with a programmed cell death ligand 1 (PD-L1) antibody (sugemalimab) alone and provide a literature review on existing ENKTCL-LAHS treatment options. A 31-year-old man with relapsed ENKTCL complicated by HLH was admitted to our hospital. Following the administration of the PD-L1 antibody sugemalimab, fever was resolved, Epstein-Barr virus (EBV) DNA copy number was negative, and HLH-related blood biochemical markers were decreased in the patient. Consequently, the patient achieved complete remission with a progression-free time (PFS) of 44 months. The prognosis of ENKTCL-LAHS is extremely poor, and the clinical treatment of ENKTCL-HLH is challenging. No previous reports exist regarding the use of PD-L1 antibodies in ENKTCL-LAHS treatment. This study is the first to report a patient with ENKTCL-LAHS treated with the PD-L1 antibody alone, who achieved a long PFS of 44 months. Our results suggest the effectiveness and safety of sugemalimab in the treatment of ENKTCL-LAHS; however, more clinical cases are required for validation. The PD-L1 antibody presents a novel treatment option for patients with ENKTCL-LAHS and warrants further clinical promotion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".